Although advances in genome wide and candidate gene association studies continue to identify common genetic variants contributing to birth defects (a leading cause of infant mortality [54]), recent evidence indicates that discovered loci only account for a small fraction of risk. As a result, researchers are shifting focus to investigae rare variants that could be responsible for this missing heritability of birth defects. The trio design (genotyping the affected child and both parents) is preferred for such association studies because it is robust to population substructure and only requires sequencing three people, rather than larger families. However established methods for analyzing common variants using trio data are hindered by reduced power when analyzing rare variants using sequence data. Thus, there is a critical need to develop methods that jointly test for common and rare variants using the trio design to comprehensively model the genetic factors associated with birth defects in order to identify the genetic variants driving the association with disease risk. Current methods for analyzing rare variants primarily focus on pooling the rare variants in a region and performing a global test for that region. To further increase power, some methods include common variants in the model as covariates. Of the few most recent methods that can statistically identify rare variants that drive the association, all are based on the case control design, which requires birth defect researchers who have been collecting trio data to find an external set of controls. This is less than ideal. There is a strong need for methods that can jointly analyze common and rare variants using trio data in such a way that can statistically identify the genetic variants (rare and common) that drive association with birth defects. We propose to fulfill this need with the following aims: (1) Develop a Bayesian stochastic search variable selection method for common and rare variant analysis using trio data that can identify the variants driving the association; and (2) Compare the performance of the global component to existing global tests for trio data using both simulated and real data for congenital heart defects. To achieve these aims, we will develop a novel Bayesian model for trio data, using the Expectation-Maximization algorithm to quickly compute the modes of the Bayesian posterior distribution as estimates for genetic regions as well as specific variants (rare and common). The proposed research is innovative because it will develop new and powerful statistical methodology. The proposed research is significant because it will produce a new powerful and widely applicable method for uncovering the genetic basis for birth defects and other childhood diseases. Ultimately, the new methods will improve birth defects research, and the application to real data can improve our understanding of the etiology of congenital heart defects, potentially improving genetic counseling and prevention strategies for congenital heart defects.